Unleashing the power of the hyphen: application of arts-informed inquiry and psychoanalytic perspectives in autoethnography to explore cultural hybridity
Bibliographic record
Abstract
In the postcolonial discourse of the social sciences, the term hybridity, most commonly used in the domains of engineering and agriculture, refers to “the [complex] interbreeding or mixing of different peoples, cultures and societies” (AlSayyd, 2001). The unique result in such multicultural societies comprising large population sub-groups, including asynchronystic waves of immigrants, is that individuals may experience identity conflict, questioning their membership in local social groups. They may painstakingly negotiate the locus or habitus (Bordieu, 1977; Mathieu, 2009) of their belonging. To explore and, in case of necessity, to better assist these individuals in coping with potential anxiety, inferiority complexes, and a sense of inadequacy within their social environments different from the loci of their cultural and linguistic origin, the proposed study uses psychoanalytic perspectives to 1) conceptualize hybridity in the context of culture, language, citizenship and power relations, 2) explore and expand the personal dimension of hybridity by applying the qualitative research that involves a cross-analysis of multiple case studies. More specifically, the data set includes biotexts, or autoethnographic narratives in para-poetic or visual formats composed by the individuals who study or work in Canada, and who consider themselves populations of dual or multiple cultural backgrounds. To make these narratives, the study participants collected their personal memos, reflective journals, and family stories. The assumption is that such pursuit helps the study populations to better recognize and voice the formative constituents of their cultural belonging and make sense of their citizenship loyalties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.064 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".